Abstract:
Cinema industry is a form of entertainment that makes the audiences dive into the movie
creators world. However, behind the scenes there are a lot of stakeholders which is the reason
why it's a risky business yet a multi billion dollar industry. While a movie can earn from selling
their movies to different online streaming services the main source of its revenue comes from
its box office collection.. In order to make sure that everyone (audiences, producers, movie
exhibitors etc) gain something an effective box office prediction is vital.
A stacked ML model will be developed where initially three models which are decision tree,
XGBoost and random forest are trained. Then it will then be used to combine the predictions
of each model which will result in a better accurate result using a meta model random forest
regressor. Moreover, for the transparency of the model XAI method SHAP will be integrated
and provide SHAP values and visualizations.
The chosen model evaluation metrics were R squared, MAE and MSE. According to the model
testing conducted, out of the three base models trained and developed random forest has been
the best performing base model by achieving a result of 0.7235 for R squared, 1.0431 for MSE
and 0.7311 for MAE. The lowest performing base model was decision tree by achieving a result
of 0.5994 for R squared, 1.5114 for MSE and 0.8802 for MAE. Whereas the meta-model also
achieved a better result of 0.6745 for R squared, 1.2278 for MSE and 0.7702 for MAE.
